Communications services have become an important part of modern life (e.g., phone service, internet service, text messaging service, paging service, GPS service, music service, gaming service, and the like), as have the devices associated with the communications (e.g., telephones, including cellular telephones, computers, notebook computers, personal digital assistants, music players, gaming systems and the like). As one example, cellular telephone usage has proliferated rapidly over the past decade. By some estimates, cellular telephone usage in the United States alone has grown from 34 million users in 1995 to over 200 million in 2005.
The term subscriber may refer to an end user of a communication service, e.g., an end-user of phone service, internet service, text messaging service, paging service, GPS service, music service, gaming service, or the like. A subscriber of mobile telephone communication service may be referred to as a mobile subscriber. For example, a mobile subscriber may be associated with a phone number that the mobile subscriber uses with a mobile communications device, such as a cellular telephone.
The terms cellular telephone and mobile telephone may be used interchangeably herein. Further, the term phone call may refer to any type of phone call (e.g. a call from/to a cellular telephone, a call from/to a land line telephone, etc.).
The term carrier may refer to an entity that provides communication services to subscribers. For example, a carrier may route calls to or from a subscriber using networks owned or operated by the carrier and/or other networks.
Mobile subscribers may enter into an agreement for services with a carrier in order to be able to use associated cellular telephones. Further, carriers may compete to attract mobile subscribers.
Subscriber metrics describe characteristics of subscribers, especially as they relate to carriers. For example, subscriber metrics may estimate carrier market share (e.g., the number of subscribers associated with a given carrier in a market as a percentage of the total number of subscribers in the market), the number of subscribers associated with a carrier in a market, carrier churn rate, carrier activation rate (or number of activations for a carrier in a given period), carrier deactivation rate (or number of deactivations for a carrier in a given period), and the like.
By analyzing subscriber metrics, carriers may be better able to utilize marketing resources. This may include being able to determine needs or preferences of subscribers and offering products or services that appeal to those needs or preferences.
Changes in subscriber metrics may happen over short periods of time. Carriers may find it beneficial to be apprised of subscriber metrics often and with little lag time. For example, if carriers are timely informed of changes in the needs or preferences of subscribers, carriers may be able to reallocate resources in response to changing subscriber needs or preferences.
Current methods of gathering and analyzing subscriber data to determine subscriber metrics have many problems. For example, consider the following examples illustrating problems with current methods of determining mobile subscriber metrics, which may also have applicability to other communications services.
Subscriber metric data for public carriers may be available quarterly, but with a significant lag time. Carriers may desire timelier subscriber metrics, subscriber metrics for different periods of time, subscriber metrics for shorter time periods, subscriber metrics with less lag time, etc. For example, carriers may desire subscriber metrics over shorter periods of time because mobile subscriber trends may take place in a much shorter period than three months. Another example is that a carrier may want timely information before starting a marketing campaign.
Currently, subscriber metric data may only be available for certain geographic regions. That is, subscriber metric data may be unavailable for regions in which a carrier may be interested, such as data on a particular county, city, sales market, etc.
Currently, another method to determine subscriber metrics is available. The other method uses sampling and/or querying to determine subscriber metrics (the “sampling/querying method”). The sampling/querying method also has many shortcomings
The sampling/querying method begins by selecting a subset of numbers owned by a carrier and monitoring those numbers (i.e., a sample is selected from the possible universe of numbers associated with a carrier). The sample selection may be made from the Local Exchange Routing Guide (LERG), which may identify numbers assigned to a carrier.
A problem with this step is that only a small subset of actual numbers is used. Using a subset of numbers as an estimate for total numbers introduces error into the resulting subscriber metrics because the resulting estimates are not based on actual data. Further, as the size of the sampled subset of numbers decreases, the error introduced into the resulting subscriber metrics may increase. Typically, the sampling/querying method uses less than 5% of actively assigned numbers.
Another problem with the sampling/querying method is that the sample set may include a variable amount of inactive numbers (i.e., a number not currently assigned to a subscriber). For example, the LERG data simply states which numbers may be assigned to a carrier. However, just because a number was assigned to a carrier does not mean that the number is an active number. By analyzing inactive numbers, resources are wasted.
Another problem with the sampling/querying method is the use of SS7 queries to confirm subscriber information. The sampling/querying method uses SS7 queries to determine information about a number. That is, under the current method an SS7 query is sent out to a device associated with the number for which the SS7 query was sent. The SS7 query typically gets information about the number from an HLR.
Using SS7 queries to determine subscriber metrics is a waste of resources. For example, by using an SS7 query one or more additional steps are needed in addition to the methods herein described. In addition an SS7 query adds an additional and unnecessary burden on communications networks because SS7 queries are routed through communications networks.
Using SS7 queries to determine subscriber metrics introduces additional inaccuracies into the subscriber metrics determined by the sampling/querying method. For example, SS7 queries determine mobile subscriber information from data associated with HLR's. However, carriers do not keep data at the HLR level up to date. Because information at HLR's may be inaccurate, using SS7 queries introduces additional inaccuracies into the subscriber metrics determined by the sampling/querying method.
Another problem with the sampling/querying method is that significant research and development must be performed before the method can be used in a new geographic region. For example, SS7 querying for a geographic region may only be meaningful if research is performed relating to the geographic region.
Thus, there exists a need to improve upon current methods of providing subscriber metrics.